Sevim, Hazan Dağlayan

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Profile URL
https://hdl.handle.net/20.500.14411/8067
Name Variants
S.,Hazan Dağlayan
Sevim, Hazan Daglayan
H., Sevim
H.,Sevim
S.,Hazan Daglayan
H.D.Sevim
Hazan Daglayan, Sevim
Hazan Dağlayan, Sevim
S., Hazan Daglayan
Sevim, Hazan Dağlayan
Sevim,H.D.
Daglayan, Hazan
Job Title
Araştırma Görevlisi
Email Address
hazan.daglayan@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Former Staff
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ORCID ID
Scopus Author ID
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WoS Researcher ID

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NO POVERTY1
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AFFORDABLE AND CLEAN ENERGY7
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
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LIFE ON LAND
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Scholarly Output

4

Articles

0

Views / Downloads

12/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

8

Scopus Citation Count

12

Patents

0

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0

WoS Citations per Publication

2.00

Scopus Citations per Publication

3.00

Open Access Source

1

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0

JournalCount
Conference on Image and Signal Processing for Remote Sensing XXII -- SEP 26-28, 2016 -- Edinburgh, SCOTLAND1
Conference on Image and Signal Processing for Remote Sensing XXI -- SEP 21-23, 2015 -- Toulouse, FRANCE1
Conference on Next-Generation Spectroscopic Technologies VIII -- APR 20-22, 2015 -- Baltimore, MD1
International Conference on Electronic Engineering and Computer Science (EECS) -- MAY 22-23, 2013 -- Beijing, PEOPLES R CHINA1
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Subject: airborne hyperspectral imaging

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Conference Object
    Citation - WoS: 6
    Citation - Scopus: 9
    Shadow Removal From Vnir Hyperspectral Remote Sensing Imagery With Endmember Signature Analysis
    (Spie-int Soc Optical Engineering, 2015) Omruuzun, Fatih; Baskurt, Didem Ozisik; Daglayan, Hazan; Cetin, Yasemin Yardimci
    This study aims to develop an effective regional shadow removal algorithm using rich spectral information existing in hyperspectral imagery. The proposed method benefits from spectral similarity of shadow and neighboring nonshadow pixels regardless of the intensity values. Although the shadow area has lower reflectance values due to inadequacy of incident light, it is expected that this area contains similar spectral characteristics with nonshadow area. Using this assumption, the endmembers in both shadowed and nonshadow area are extracted by Vertex Component Analysis (VCA). On the other hand, HySime algorithm overcomes estimating number of endmembers, which is one of the challenging parts in hyperspectral unmixing. Therefore, two sets of endmembers are extracted independently for both shadowed and nonshadow area. The proposed study aims at revealing the relation between these two endmember sets by comparing their pairwise similarities. Finally, reflectance values of shadowed pixels are re-calculated separately for each spectral band of hyperspectral image using this information.